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Notes on language models
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Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs
Researchers have developed two new mechanisms - Reinforcement Learning with Metacognitive Feedback (RLMF) and Metacognitive Data Selection - to improve the performance of Language Models (LLMs). The goal is to make…
Self-Study Reconsidered: The Hidden Fragility of Learning from Self-Generated QA
The authors investigate the generation of question-answer (QA) pairs used for training language models and find that this process is not neutral as it can introduce biases due to the selection of evidence and the…
When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors
The research evaluates the issue of data referencing errors (DREs) made by large language models (LLMs) while performing table tasks. The study presents a systematic evaluation of these errors across different models…
Retrieval Backward Attention without Additional Training: Enhance Embeddings of Large Language Models via Repetition
Researchers propose a method called ReBA (Retrieval Backward Attention) to enhance the quality of word-level embeddings produced by pretrained language models without affecting sentence-level representations…
Symbiotic Cooperation for Web Agents: Harnessing Complementary Strengths of Large and Small LLMs
Researchers propose AgentSymbiotic, an iterative framework that enhances both large and small Language Model (LLM) performance in web browsing tasks by combining data synthesis with task-performance. Large LLMs…
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Auditing Framing-Sensitive Behavioral Instability in Large Language Models for Mental Health Interactions
This research investigates how different contexts (framings) can affect the responses of large language models used in mental health support tools. The study finds that framing does systematically alter interpretive…
Diagnosing Task Insensitivity in Language Agents
Researchers identify an issue with large language models where they are unable to adapt to new tasks effectively due to their task insensitivity. The authors propose a lightweight solution called Task-Perturbed NLL…
In-Context Model Predictive Generation: Open-Vocabulary Motion Synthesis from Language Models to Physics
The paper proposes a new method called In-Context Model Predictive Generation (ICMPG) for generating human motions based on textual descriptions in digital applications. The method combines language model planning with…
In-Context Model Predictive Generation: Open-Vocabulary Motion Synthesis from Language Models to Physics
Researchers propose a new method called In-Context Model Predictive Generation (ICMPG) for generating realistic human motions from textual descriptions. The method combines language-model planning with physical…
Inherited Circuits, Learned Semantics: How Fine-Tuning Creates Evasion Vulnerabilities Invisible to Standard Evaluation
The study reveals that Language Models (LLMs) fine-tuned for security classification can overlook vulnerabilities introduced during fine-tuning itself. These models may learn specific token-level indicators which…
Language-Based Digital Twins for Elderly Cognitive Assistance
The paper proposes a language-based digital twin framework that uses large language models to mimic the conversational behavior of elderly individuals, helping in early detection of Mild Cognitive Impairment (MCI). A…
When Does Combining Language Models Help? A Co-Failure Ceiling on Routing, Voting, and Mixture-of-Agents Across 67 Frontier Models
The text discusses multi-model language model systems and their potential to outperform single-models in terms of accuracy. The study shows that the gain is limited by a quantity called 'beta', which represents the…
When Does Combining Language Models Help? A Co-Failure Ceiling on Routing, Voting, and Mixture-of-Agents Across 67 Frontier Models
The study investigates the use of multi-model language models to improve accuracy over single-models. It is found that the improvement is limited by a factor called 'beta', which represents the rate at which all models…
Where Do CoT Training Gains Land in LLM based Agents?
The study investigates how Chain-of-thought (CoT) training in language model agents affects their performance. The authors find that while CoT improves overall, it doesn't significantly widen the advantage of CoT…
Where Do Models Find Happiness? Emotion Vectors in Open-Source LLMs
Researchers have found that two open-source language models, Apertus-8B-Instruct-2509 and Gemma-4-E4B-it, have internal representations (emotion vectors) that can encode emotions such as valence and arousal. The…
MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources
Researchers propose MiniOpt, a reinforcement learning framework that helps large language models solve various optimization problems with limited resources. It breaks down optimization reasoning into structured…
Probabilistic Agents in Deterministic Audits: Evaluating Multi-Agent Systems for Automated Audits Based on the German IT-Grundschutz
This research paper presents a Multi-Agent System (MAS) architecture with Hybrid Retrieval Augmented Generation (HybridRAG) for partially automating IT-Grundschutz certification, a German standard for IT security. The…
Probabilistic Agents in Deterministic Audits: Evaluating Multi-Agent Systems for Automated Audits Based on the German IT-Grundschutz
This research presents the implementation and evaluation of a Multi-Agent System (MAS) architecture combined with Hybrid Retrieval Augmented Generation (HybridRAG) for partially automating IT-GS certification, which is…
SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment
Researchers have proposed a framework called SARA (Semantically Anchored Routing Alignment) to improve the performance of multilingual language models in low-resource languages. The framework aligns the routing…
A specialized reasoning large language model for accelerating rare disease diagnosis: a randomized AI physician assistance trial
A new open-source language model called RaDaR (Rare Disease navigatoR) has been developed to aid in the diagnosis of rare diseases. The model was trained on a combination of public free-text cases and synthetic cases…
AdversaBench: Automated LLM Red-Teaming with Multi-Judge Confirmation and Cross-Model Transferability
The paper introduces AdversaBench, a red-teaming pipeline for evaluating large language models. The pipeline generates hard inputs using five structured operators and confirms failures through a three-judge panel with…
Can Scale Save Us From Plasticity Loss in Large Language Models?
Researchers studied plasticity loss, a challenge in creating neural networks capable of continuous learning, in modern transformer-based language models (LLMs). They found evidence of plasticity loss across various…
Governed Shared Memory for Multi-Agent LLM Systems
The paper discusses the challenges in managing shared knowledge in multi-agent language models (LLMs) environments and proposes solutions to prevent four failure modes: unauthorized leakage, stale propagation…
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